Example embodiments of the present disclosure provide for an example method for controlling the activity of a production facility, such as a production facility having one or more automation environments. The example method includes receiving data indicative of a current production environment. The data can include data of a sensor representing a time since last unit or fill level at one or more processing stations in a production facility. The example method can include determining an impact probability of a downtime event based at least in part on data indicative of the current production environment. The example method can include determining the impact probability of a downtime event and performing a control action associated with the production facility in response to determining the impact probability of the downtime event.

A production information management system includes: a storage device that stores 4M (man, machine, material, and method) data information including time series data in which a state of each element of 4M per unit time is associated with acquisition accuracy of 4M data defined for each target and acquisition method of 4M, and analysis model information defining a criterion for determining a production loss from a combination of the 4M data information; a processor that analyzes the 4M data information by the analysis model information to estimate a production loss, and calculates estimation accuracy for the each production loss to generate production loss information; and a production loss display unit that displays the production loss information.

A method for tracking the downtime of a production machine (12) comprises the steps of: Receiving sensor data (SD) from the production machine (12) and production target data (PTD), Combining the sensor data (SD) and the production target data over a certain period (40) providing combined data (D) and calculating characteristic data (CD) of the combined data (D), Determining if the combined data (D) is from a downtime period (44) of the production machine (12) based on the characteristic data (CD), and Characterizing the downtime period (44) using a machine learning module (48) implemented in the control unit (32), the machine learning module (48) providing a reason (R) for the downtime period (44) as an output value. Further, a system (19) for tracking the downtime of a production machine (12) is shown.